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Original Articles

Incremental 4D-Var convergence study

Pages 706-718 | Received 23 Feb 2007, Accepted 02 Jul 2007, Published online: 15 Dec 2016

Keep up to date with the latest research on this topic with citation updates for this article.

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Michael Goodliff, Javier Amezcua & Peter Jan Van Leeuwen. (2017) A weak-constraint 4DEnsembleVar. Part II: experiments with larger models. Tellus A: Dynamic Meteorology and Oceanography 69:1.
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S. A. Haben, A.S. Lawless & N.K. Nichols. (2011) Conditioning of incremental variational data assimilation, with application to the Met Office system. Tellus A: Dynamic Meteorology and Oceanography 63:4, pages 782-792.
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Articles from other publishers (39)

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Tae-Kwon Wee, Richard A. Anthes, Douglas C. Hunt, William S. Schreiner & Ying-Hwa Kuo. (2022) Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation. Remote Sensing 14:21, pages 5614.
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Xiaohui Wang, Martin Verlaan, Jelmer Veenstra & Hai Xiang Lin. (2022) Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model. Ocean Science 18:3, pages 881-904.
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Xiaohui Wang, Martin Verlaan, Maialen Irazoqui Apecechea & Hai Xiang Lin. (2022) Parameter estimation for a global tide and surge model with a memory-efficient order reduction approach. Ocean Modelling 173, pages 102011.
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Jemima M. Tabeart, Sarah L. Dance, Amos S. Lawless, Nancy K. Nichols & Joanne A. Waller. (2021) New bounds on the condition number of the Hessian of the preconditioned variational data assimilation problem. Numerical Linear Algebra with Applications 29:1.
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Yann Michel & Pierre Brousseau. (2021) A Square-Root, Dual-Resolution 3DEnVar for the AROME Model: Formulation and Evaluation on a Summertime Convective Period. Monthly Weather Review 149:9, pages 3135-3153.
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Xiaohui Wang, Martin Verlaan, Maialen Irazoqui Apecechea & Hai Xiang Lin. (2021) Computation‐Efficient Parameter Estimation for a High‐Resolution Global Tide and Surge Model. Journal of Geophysical Research: Oceans 126:3.
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O. L. Quintero Montoya, Elías D. Niño-Ruiz & Nicolás Pinel. (2020) On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes. Environmental Science and Pollution Research 27:29, pages 35993-36012.
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P. Laloyaux, M. Bonavita, M. Dahoui, J. Farnan, S. Healy, E. Hólm & S. T. K. Lang. (2020) Towards an unbiased stratospheric analysis. Quarterly Journal of the Royal Meteorological Society 146:730, pages 2392-2409.
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Elias D. Nino-Ruiz, Luis G. Guzman-Reyes & Rolando Beltran-Arrieta. (2019) An adjoint-free four-dimensional variational data assimilation method via a modified Cholesky decomposition and an iterative Woodbury matrix formula. Nonlinear Dynamics 99:3, pages 2441-2457.
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Yongzhu Liu, Lin Zhang & Zhihua Lian. (2019) Conjugate Gradient Algorithm in the Four-Dimensional Variational Data Assimilation System in GRAPES. Journal of Meteorological Research 32:6, pages 974-984.
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François Mercier, Selime Gürol, Pierre Jolivet, Yann Michel & Thibaut Montmerle. (2018) Block Krylov methods for accelerating ensembles of variational data assimilations. Quarterly Journal of the Royal Meteorological Society 144:717, pages 2463-2480.
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Massimo Bonavita, Peter Lean & Elias Holm. (2018) Nonlinear effects in 4D-Var. Nonlinear Processes in Geophysics 25:3, pages 713-729.
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Azam Moosavi, Răzvan Ştefănescu & Adrian Sandu. (2018) Multivariate predictions of local reduced-order-model errors and dimensions. International Journal for Numerical Methods in Engineering 113:3, pages 512-533.
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François-Xavier Le Dimet, Ionel M. Navon & Răzvan Ştefănescu. 2017. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III). Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III) 1 53 .
Craig H. Bishop, Sergey Frolov, Douglas R. Allen, David D. Kuhl & Karl Hoppel. (2017) The Local Ensemble Tangent Linear Model: an enabler for coupled model 4D‐Var . Quarterly Journal of the Royal Meteorological Society 143:703, pages 1009-1020.
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Alexandru Cioaca. (2015) Advanced HPC methods for large-scale sensitivity analysis. Advanced HPC methods for large-scale sensitivity analysis.
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S. Kim, B.-J. Jung & Y. Jo. (2014) Development of a tangent linear model (version 1.0) for the High-Order Method Modeling Environment dynamical core. Geoscientific Model Development 7:3, pages 1175-1182.
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P. L. Houtekamer, Xingxiu Deng, Herschel L. Mitchell, Seung-Jong Baek & Normand Gagnon. (2014) Higher Resolution in an Operational Ensemble Kalman Filter. Monthly Weather Review 142:3, pages 1143-1162.
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S. Kim, B.-J. Jung & Y. Jo. (2014) Development of a tangent linear model (version 1.0) for the high-order method modelling environment dynamical core. Geoscientific Model Development Discussions 7:1, pages 1175-1196.
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Alexandru Cioaca, Adrian Sandu & Eric de Sturler. (2013) Efficient methods for computing observation impact in 4D-Var data assimilation. Computational Geosciences 17:6, pages 975-990.
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R. J. J. Stappers & J. Barkmeijer. (2013) Gaussian quadrature 4D-Var. Quarterly Journal of the Royal Meteorological Society 139:675, pages 1462-1472.
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Amal El Akkraoui, Yannick Trémolet & Ricardo Todling. (2013) Preconditioning of variational data assimilation and the use of a bi-conjugate gradient method. Quarterly Journal of the Royal Meteorological Society 139:672, pages 731-741.
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S.A. Haben, A.S. Lawless & N.K. Nichols. (2011) Conditioning and preconditioning of the variational data assimilation problem. Computers & Fluids 46:1, pages 252-256.
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Dacian N. Daescu & Ricardo Todling. (2010) Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters. Quarterly Journal of the Royal Meteorological Society 136:653, pages 2000-2012.
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N. Papadakis & E. Mémin. (2008) A Variational Technique for Time Consistent Tracking of Curves and Motion. Journal of Mathematical Imaging and Vision 31:1, pages 81-103.
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J. Tshimanga, S. Gratton, A. T. Weaver & A. Sartenaer. (2008) Limited-memory preconditioners, with application to incremental four-dimensional variational data assimilation. Quarterly Journal of the Royal Meteorological Society 134:632, pages 751-769.
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